Paper summaryshagunsodhani#### Introduction
* The paper presents a domain agnostic approach for conversational modelling based on [Sequence to Sequence Learning Framework](https://gist.github.com/shagunsodhani/e3608ccf262d6e5a6b537128c917c92c).
* [Link to the paper](http://arxiv.org/abs/1506.05869)
#### Model
* Neural Conversational Model (NCM)
* A Recurrent Neural Network (RNN) reads the input sentence, one token at a time, and predicts the output sequence, one token at a time.
* Learns by backpropagation.
* The model maximises the cross entropy of correct sequence given its context.
* Greedy inference approach where predicted output token is used as input to predict the next output token.
#### Dataset
* IT HelpDesk dataset of conversations about computer related issues.
* OpenSubtitles dataset containing movie conversations.
#### Results
* The paper has reported some samples of conversations generated by the interaction between human actor and the NCM.
* NCM reports lower perplexity as compared to n-grams model.
* NCM outperforms CleverBot in a subjective test involving human evaluators to grade the two systems.
#### Strengths
* Domain-agnostic.
* End-To-End training without handcrafted rules.
* Underlying architecture (Sequence To Sequence Framework) can be leveraged for machine translation, question answering etc.
#### Weakness
* The responses are simple, short and at times inconsistent.
* The objective function of Sequence To Sequence Framework is not designed to capture the objective of conversational models.

TLDR; The authors train a seq2seq model on conversations, building a chat bot. The first data set is an IT Helpdesk dataset with 33M tokens. The trained model can help solve simple IT problems. The second data set is the OpenSubtitles data with ~1.3B tokens (62M sentences). The resulting model learns simple world knowledge, can generalize to new questions, but lacks a coherent personality.
#### Key Points
- IT Helpdesk: 1-layer LSTM, 1024-dimensional cells, 20k vocabulary. Perplexity of 8.
- OpenSubtitles: 2-layer LSTM, 4096-dimensional cells, 100k vocabulary, 2048 affine layer. Attention did not help.
- OpenSubtitles: Treat two consecutive sentences as coming from different speakers. Noisy dataset.
- Model lacks personality, gives different answers to similar questions (What do you do? What's your job?)
- Feed previous context (whole conversation) into encoder, for IT data only.
- In both data sets, the neural models achieve better perplexity than n-gram models.
#### Notes / Questions
- Authors mention that Attention didn't help in OpenSubtitles. It seems like the encoder/decoder context is very short (just two sentences, not a whole conversation). So perhaps attention doesn't help much here, as it's meant for long-range dependencies (or dealing with little data?)
- Can we somehow encode conversation context in a separate vector, similar to paragraph vectors?
- It seems like we need a principled way to deal with long sequences and context. It doesn't really make sense to treat each sentence tuple in OpenSubtitles as a separate conversation. Distant Supervision based on subtitles timestamps could also be interesting, or combine with multimodal learning.
- How we can learn a "personality vector"? Do we need world knowledge or is it learnable from examples?

#### Introduction
* The paper presents a domain agnostic approach for conversational modelling based on [Sequence to Sequence Learning Framework](https://gist.github.com/shagunsodhani/e3608ccf262d6e5a6b537128c917c92c).
* [Link to the paper](http://arxiv.org/abs/1506.05869)
#### Model
* Neural Conversational Model (NCM)
* A Recurrent Neural Network (RNN) reads the input sentence, one token at a time, and predicts the output sequence, one token at a time.
* Learns by backpropagation.
* The model maximises the cross entropy of correct sequence given its context.
* Greedy inference approach where predicted output token is used as input to predict the next output token.
#### Dataset
* IT HelpDesk dataset of conversations about computer related issues.
* OpenSubtitles dataset containing movie conversations.
#### Results
* The paper has reported some samples of conversations generated by the interaction between human actor and the NCM.
* NCM reports lower perplexity as compared to n-grams model.
* NCM outperforms CleverBot in a subjective test involving human evaluators to grade the two systems.
#### Strengths
* Domain-agnostic.
* End-To-End training without handcrafted rules.
* Underlying architecture (Sequence To Sequence Framework) can be leveraged for machine translation, question answering etc.
#### Weakness
* The responses are simple, short and at times inconsistent.
* The objective function of Sequence To Sequence Framework is not designed to capture the objective of conversational models.